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Social Navigation for Mobile Robots in the Emergency Department

Social Navigation for Mobile Robots in the Emergency Departm...

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IEEE international conference on robotics and automation (ICRA)

作者： Taylor, Angelique M. Matsumoto, Sachiko Xiao, Wesley Riek, Laurel D. Univ Calif San Diego Comp Sci & Engn La Jolla CA 92093 USA

ISBN: (纸本)9781728190778

The emergency department (ED) is a safety-critical environment in which healthcare workers (HCWs) are overburdened, overworked, and have limited resources, especially during the COVID-19 pandemic. One way to address this problem is to explore the use of robots that can support clinical teams, e.g., to deliver materials or restock supplies. However, due to EDs being overcrowded, and the cognitive overload HCWs experience, robots need to understand various levels of patient acuity so they avoid disrupting care delivery. In this paper, we introduce the Safety-Critical Deep Q-Network (SafeDQN) system, a new acuity-aware navigation system for mobile robots. SafeDQN is based on two insights about care in EDs: high-acuity patients tend to have more HCWs in attendance and those HCWs tend to move more quickly. We compared SafeDQN to three classic navigation methods, and show that it generates the safest, quickest path for mobile robots when navigating in a simulated ED environment. We hope this work encourages future exploration of social robots that work in safety-critical, human-centered environments, and ultimately help to improve patient outcomes and save lives.

关键词： COVID-19 automation Navigation Pandemics conferences Medical services Mobile robots

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Optimized Admittance Control for Manipulators Interacting with Unknown Environment

Optimized Admittance Control for Manipulators Interacting wi...

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IEEE international conference on Industrial Technology (ICIT)

作者： Haiyi Kong Guangzhu Peng Guang Li Chenguang Yang Department of Electrical and Electronic Engineering University of Manchester Oxford Rd Manchester UK Bristol Robotics Laboratory University of the West of England Bristol UK School of Automation Nanjing University of Information Science and Technology Nanjing China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.

关键词： Adaptation models Control design Heuristic algorithms End effectors Dynamic programming Trajectory Iterative methods

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Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained models

Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Mo...

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IEEE international conference on robotics and automation (ICRA)

作者： Wen-Hsuan Chu Adam W. Harley Pavel Tokmakov Achal Dave Leonidas Guibas Katerina Fragkiadaki Carnegie Mellon University Stanford University Toyota Research Institute

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Object tracking is central to robot perception and scene understanding, allowing robots to parse a video stream in terms of moving objects with names. Tracking-by-detection has long been a dominant paradigm for object tracking of specific object categories [1], [2]. Recently, large-scale pre-trained models have shown promising advances in detecting and segmenting objects and parts in 2D static images in the wild. This raises the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking? In this paper, we combine an open-vocabulary detector [3], segmenter [4], and dense optical flow estimator [5], into a model that tracks and segments any object in 2D videos. Given a monocular video input, our method predicts object and part mask tracks with associated language descriptions, rebuilding the pipeline of Tractor [6] with modern large pre-trained models for static image detection and segmentation: we detect open-vocabulary object instances and propagate their boxes from frame to frame using a flow-based motion model, refine the propagated boxes with the box regression module of the visual detector, and prompt an open-world segmenter with the refined box to segment the objects. We decide the termination of an object track based on the objectness score of the propagated boxes as well as forward-backward optical flow consistency. We re-identify objects across occlusions using deep feature matching. We show that our model achieves strong performance on multiple established benchmarks [7], [8], [9], [10], and can produce reasonable tracks in manipulation data [11]. In particular, our model outperforms previous state-of-the-art in UVO and BURST, benchmarks for open-world object tracking and segmentation, despite never being explicitly trained for tracking. We hope that our approach can serve as a simple and extensible framework for future research and enable imitation learning from videos with unconventional objects.

关键词： Image segmentation Visualization Video tracking Motion segmentation Benchmark testing Streaming media Predictive models

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Intelligent Security Q&A System Based on Large Language Model

Intelligent Security Q&A System Based on Large Language Mode...

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robotics, Artificial Intelligence and Intelligent Control (RAIIC), international conference on

作者： Youtao Zhou Qiuhong Lu Haoyu Fan Yuntao Xiao Jinwen Hu Shimian Zhang The School of Automation Engineering University of Electric Power Shanghai China Shanghai HRSTEK Co. Ltd Shanghai China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

With the continuous development of artificial intelligence technology, intelligent Q&A systems have been widely used in various fields, including hotels, airports, hospitals and other places. However, traditional Q&A systems are often constrained by the limited rule base and stereotypical dialog flow, and are unable to provide a sufficiently intelligent interaction experience. The system in this paper utilizes Large Language Model (LLM) combined with face recognition technology to build a more intelligent and flexible security Q&A system. By combining the local knowledge base with LLM, the system is able to retrieve relevant information from the local knowledge base based on the professional questions provided by the user through text vectorization and then similarity comparison, and generate corresponding responses, thus providing a more humanized and accurate service. The system has the following capabilities: (1) specialized Q&A, based on LLM and self-constructed knowledge base to generate Q&A with domain-specific knowledge, compared to model fine-tuning methods, this method can deploy large vertical domain models without re-training;(2) Security welcome, this system face recognition module adopts Python+Dlib method to identify the user and conduct the welcome greeting, if the visitor is an unfamiliar user, it is not possible to conduct the Q&A with the LLM for the sensitive information, e.g., private information, etc.; if the visitor is a known user, then it is possible to communicate with the LLM in all aspects.

关键词： Large language models Face recognition Knowledge acquisition Knowledge based systems Natural languages Programming User experience

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HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF Point Cloud Registration

HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF...

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IEEE international conference on robotics and automation (ICRA)

作者： Adam Misik Driton Salihu Xin Su Heike Brock Eckehard Steinbach Siemens Technology Munich Germany School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Given its wide application in robotics, point cloud registration is a widely researched topic. Conventional methods aim to find a rotation and translation that align two point clouds in 6 degrees of freedom (DoF). However, certain tasks in robotics, such as category-level pose estimation, involve non-uniformly scaled point clouds, requiring a 9DoF transform for accurate alignment. We propose HEGN, a novel equivariant graph neural network for 9DoF point cloud registration. HEGN utilizes equivariance to rotation, translation, and scaling to estimate the transformation without relying on point correspondences. Based on graph representations for both point clouds, we extract equivariant node features aggregated in their local, cross-, and global context. In addition, we introduce a novel node pooling mechanism that leverages the cross-context importance of nodes to pool the graph representation. By repeating the feature extraction and node pooling, we obtain a graph hierarchy. Finally, we determine rotation and translation by aligning equivariant features aggregated over the graph hierarchy. To estimate scaling, we leverage scale information in the vector norm of the equivariant features. We evaluate the effectiveness of HEGN through experiments with the synthetic ModelNet40 dataset and the real-world ScanObjectNN dataset. The results show the superior performance of HEGN in 9DoF point cloud registration and its competitive performance in conventional 6DoF point cloud registration.

关键词： Point cloud compression Three-dimensional displays Pose estimation Transforms Feature extraction Graph neural networks Vectors

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methods of Signal to Image Transformation in Photovoltaic Fault Diagnosis in Preparation for Machine Learning Applications

Methods of Signal to Image Transformation in Photovoltaic Fa...

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robotics, Engineering, Science, and Technology (RESTCON), international conference on

作者： Rolando Pula Lorena Ilagan Marcelo Santos Electronics Department College of Engineering University of Perpetual Help System DALTA Las Piñas City Philippines

This study explores various techniques for transforming 1-dimensional time-series data into 2-dimensional images, preparing for the application of machine learning models designed for 2D data. Eight distinct methods are introduced, including recurrence plots, Markov transition, Gramian angular field, spectrogram, heatmap, direct plot, phase space transformation, and Poincaré plots. These methods are tested using data from a modeled photovoltaic (PV) grid-connected system, specifically simulating a shorted string fault and a no-fault condition. The fault and no-fault responses are captured with a fixed window size of 256 sample points, consistently applied across all methods. All transformation method is tested through python 3 programming using a laptop with minimal computing capability. The generated image of each transformation may contain 1-channel image in grayscale or 3-channel RGB image. Dimension of the generated image can be increase or decrease during saving process. Each method produces a unique visual representation of the shorted string fault and a no-fault, demonstrating diverse perspectives in transforming 1D time-series data into 2D images for subsequent machine learning applications.

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Creating a Model of a Class of Hybrid Streaming Systems

Creating a Model of a Class of Hybrid Streaming Systems

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international Russian automation conference (RusAutoCon)

作者： Nikolay Kiselev Dept. nformation technologies and intelligent systems State Power Engineering University Kazan Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

Analysis and design of complex technical systems in the field of electrical and thermal power engineering requires modeling of both continuous processes occurring in these systems and discrete control processes associated with switching and setting various operating modes. models describing such systems belong to the class of continuous-discrete ones. The paper considers a number of formal models. It is shown that there are no methods for modeling and analyzing continuous-discrete systems in which methods for studying discrete and continuous components would equally coexist. The article shows a method for constructing a matrix model, which is formed using a circuit graph. The basis is the adjacency matrix and the vector of functions. The proposed matrix model is used to solve differential and logical equations describing continuous and discrete processes, respectively. The problems of coupling continuous and discrete state models are shown. methods for solving high-dimensional matrix equations are considered.

关键词： Analytical models Computational modeling Heuristic algorithms Thermal engineering Process control Mathematical models Vectors Thermal analysis Integrated circuit modeling Switching circuits

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Force control for Robust Quadruped Locomotion: A Linear Policy Approach

Force control for Robust Quadruped Locomotion: A Linear Poli...

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IEEE international conference on robotics and automation (ICRA)

作者： Aditya Shirwatkar Vamshi Kumar Kurva Devaraju Vinoda Aman Singh Aditya Sagi Himanshu Lodha Bhavya Giri Goswami Shivam Sood Ketan Nehete Shishir Kolathaya Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bengaluru Department of Computer Science & Automation Indian Institute of Science Bengaluru Indian Institute of Technology Kharagpur Department of Computer Science & Automation Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bengaluru

This work presents a simple linear policy for direct force control for quadrupedal robot locomotion. The motivation is that force control is essential for highly dynamic and agile motions. We learn a linear policy to generate end-foot trajectory parameters and a centroidal wrench, which is then distributed among the legs based on the foot contact information using a quadratic program (QP) to get the desired ground reaction forces. Unlike the majority of the existing works that use complex nonlinear function approximators to represent the RL policy or model predictive control (MPC) methods with many optimization variables in the order of hundred, our controller uses a simple linear function approximator to represent policy along with only a twelve variable QP for the force distribution. A centroidal dynamics-based MPC method is used to generate reference trajectory data, and then the linear policy is trained using imitation learning to minimize the deviations from the reference trajectory. We demonstrate this compute-efficient controller on our robot Stoch3 in simulation and real-world experiments on indoor and outdoor terrains with push recovery.

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ConsistencyPure: Consistency models for Realtime Adversarial Purification

ConsistencyPure: Consistency Models for Realtime Adversarial...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yiming Liu Kezhao Liu Yuchen Liu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Diffusion-Based Purification (DBP) is a stochasticity-based defense method that effectively mitigates adversarial attacks but suffers from high computational costs due to its iterative reverse process. To address this, we propose ConsistencyPure, a novel purification framework leveraging a pre-trained consistency model distilled from diffusion models. ConsistencyPure requires only one forward and one reverse sampling step, achieving real-time purification while maintaining comparable defense performance and improving efficiency by one to two orders of magnitude. Our finding further confirms that the robustness of DBP stems primarily from randomness, independent of the diffusion framework’s iterative operations or noise prediction. Importantly, we highlight for the first time the crucial role of model representation learning in stochasticity- based defense methods. Extensive experiments validate the effectiveness and practicality of ConsistencyPure.

关键词： Representation learning Purification Computational modeling Noise Diffusion models Real-time systems Robustness Computational efficiency Iterative methods Robots

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites 4

Real-time In-situ Defect Detection in 3D Printing of Continu...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yue, Zhijian Shen, Zhen Dong, Xiangyang Fang, Qihang Wang, Weixing Dong, Xisong Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach. © 2024 IEEE.

关键词： Fiber reinforced plastics

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